Artificial Intelligence in the Chemical Industry: Preserving, Sharing, and Making Knowledge Usable

The chemical industry thrives on knowledge—knowledge of formulations, raw materials, process parameters, quality requirements, and regulatory guidelines. Many decisions are based on the experience that people have built up over years or even decades.

Yet it is precisely this knowledge that is coming under increasing pressure. A shortage of skilled workers, generational change, and the growing complexity of the industry are making it increasingly difficult to preserve this experience and ensure it remains accessible throughout the company.

While discussions focus on artificial intelligence, automation, and new technologies, chemical companies are therefore often faced with another question:

How can existing knowledge be secured for the long term and made available where decisions are made?

The chemical industry has always been knowledge-intensive. Many decisions are not based solely on master data, formulas, or work instructions. They are based on practical experience.

Why was a formula adjusted a few years ago? Why was a certain raw material replaced? What measure has already proven effective in addressing a similar quality deviation? Although this information often exists somewhere within the company, it is not always easy to find.

At the same time, industry requirements are constantly increasing. Product variants are on the rise, regulatory requirements are becoming more extensive, and production processes are growing in complexity. Experienced employees are leaving the company, while new specialists need to be trained.

This creates a risk that is often underestimated: It is not missing data that becomes a problem, but rather lost or hard-to-access knowledge.

In chemical companies, knowledge isn’t generated in a single location. It is generated throughout the entire value chain.

Formulations are tested and refined in the laboratory. Process parameters are optimized in production. Quality assurance documents tests and deviations. Purchasing and R&D evaluate new raw material alternatives. Sales and customer service gain important insights into how the company’s products are used by customers, what requirements arise, and how markets are evolving.

Each of these areas generates valuable information every day.

The challenge, therefore, is not to generate knowledge. The challenge is to make this knowledge usable for the entire company.

Most companies do not lack information. On the contrary.

Batch data, certificates of analysis, lab reports, test records, manufacturing instructions, and audit records are already available today. However, this information is often scattered across ERP systems, quality management solutions, document management systems, collaboration platforms, emails, or local storage systems.

The real challenge, therefore, is often not:

“Do we have the information?”

But rather:

“How do we find the right information in the right context?”

For example, a quality manager might be looking for comparable quality deviations from the past. A production manager might want to understand which process parameters were used for a similar batch. A development team might need insights from previous formulation adjustments.

Traditional search functions return documents.

Employees often still have to piece together the actual answer themselves.

This is precisely where one of the most exciting areas of application for artificial intelligence in the chemical industry emerges.

AI doesn’t just automate processes. It transforms access to knowledge.

Instead of manually searching through multiple systems, folder structures, or documents, employees can ask questions in natural language. The AI searches relevant information sources, links content, and presents the results in a technical context.

This creates context from individual pieces of information.

A quality manager receives not only an analysis certificate but also the associated batch information, test reports, and documented actions. Production managers gain insights into comparable process situations more quickly. Management and controlling identify correlations between quality, production, and cost-effectiveness.

The real added value here does not lie in providing more information. The added value lies in making existing knowledge usable more quickly.

Already today, AI assistants are helping employees research information, summarize content, or complete tasks more efficiently.

Looking ahead, the technology will continue to evolve. Future AI solutions will not only be able to find information, but will also increasingly be able to structure and evaluate it, as well as prepare for specific tasks.

The goal is not to replace human decision-making. Rather, Agentic AI can consolidate information from various sources, prepare tasks, and support employees in making more informed decisions.

Such approaches can play a particularly important role in an industry characterized by high complexity and extensive documentation requirements.

As exciting as AI is, its potential remains limited without a robust data foundation.

AI can only access knowledge that is digitally available, structured, and accessible. That’s why the path to successfully using AI doesn’t start with the technology, but with the data and processes.

For chemical companies, the ERP system often serves as the foundation. Unlike in other industries, it’s not just traditional business data that’s generated here. Formulas, batch information, quality inspections, certificates of analysis, production data, and business metrics are also part of the company’s knowledge foundation.

With COSMO ERP Chemicals, based on Microsoft Dynamics 365 Business Central, precisely this information can be consolidated into a single, shared database. Formula management, batch tracking, production planning, quality assurance, and compliance processes are all interconnected. This creates the foundation on which knowledge can be systematically harnessed.

Through integration with the Microsoft platform, additional solutions such as Microsoft 365, Microsoft Power BI, Microsoft Fabric, the Microsoft Power Platform, and Microsoft Copilot can be added. Step by step, this creates an end-to-end platform for data, analytics, and AI.

Many companies wonder how they can successfully get started with AI.

The answer is often not: with a large-scale AI project.

It makes more sense to first examine existing knowledge gaps and the time spent on research. Where do employees spend a lot of time researching? Where do media breaks occur? Where does valuable knowledge remain hidden in specific areas?

This is exactly where the COSMO CONSULT AI Framework comes in. This structured consulting approach guides companies through four sequential phases: Inspiration, Foundation, Automation, and Evolution. The focus is not on technologies, but on specific use cases, data quality, governance, and sustainable scaling.

The goal is not to implement AI as quickly as possible. The goal is to deploy it where it creates measurable added value.

The greatest opportunity for artificial intelligence in the chemical industry does not lie in replacing people.

It lies in making knowledge actionable.

Companies already possess enormous amounts of process, quality, compliance, and experiential knowledge. The challenge is to capture this knowledge, make it available across departments, and provide it where decisions are made.

With an integrated database, modern platforms, and a structured approach, AI can provide exactly that support.

After all, in the chemical industry, knowledge is often just as valuable as the raw material itself. And the companies that successfully capture and utilize this knowledge lay the foundation for their future.